June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
The impact on staff efficiency of implementing a DICOM-compatible workflow in an academic ophthalmology practice
Author Affiliations & Notes
  • Ravi Pandit
    Glaucoma Center of Excellence, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD
    Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
  • Michael Boland
    Glaucoma Center of Excellence, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD
    Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD
  • Footnotes
    Commercial Relationships Ravi Pandit, None; Michael Boland, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 2317. doi:
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      Ravi Pandit, Michael Boland; The impact on staff efficiency of implementing a DICOM-compatible workflow in an academic ophthalmology practice. Invest. Ophthalmol. Vis. Sci. 2013;54(15):2317.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

Despite the availability of Digital Imaging and Communications in Medicine (DICOM) standards for the major ophthalmic testing modalities, clinic workflow remains largely unchanged. Staff enter patient demographic data into each machine, and these multiple points of entry result in errors that pose efficiency and patient safety challenges. Radiology has demonstrated that DICOM-based imaging workflow can overcome these problems, so we set out to determine the impact of DICOM workflow on an ophthalmology practice.

 
Methods
 

Two evaluations were conducted both before and after deployment of a DICOM-compatible image management system (Forum, Carl Zeiss Meditec.) First, over a two-week period, clinic staff recorded the work required to enter, confirm, or edit patient demographics at each device. Second, we determined the proportion of tests sent to an error queue for manual reconciliation because of incorrect demographic information. Prior to the implementation of the DICOM workflow, staff were entering and editing patient information directly on each device. Once deployed, the DICOM archive received demographic information from the clinic’s patient registration system and then used these data to create work lists for each of 12 imaging and visual field devices. After implementation of the DICOM workflow, staff merged patient records in the archive (when necessary) and then selected the appropriate patient from the work list at each testing device.

 
Results
 

Staff entered, edited, or merged data for 48% of patients prior to implementation (n=237). This fell to 24% shortly after implementing the DICOM archive (n=230). Once prior records had been merged, staff could locate a patient in the DICOM work list at the device 95% of the time. Prior to the implementation, 9.2% of the images required additional intervention to be associated with the correct patient (n =3581). At 3 months after implementation, this dropped to 4.3% (n=8635; p<0.001) and fell to 1.4% at 6 months (n=9976; p < 0.001).

 
Conclusions
 

Implementation of a DICOM-compatible workflow reduced the need to enter or edit patient demographic information by 50% and reduced the need to manage misfiled images by 85%. In a clinical environment that demands both efficiency and patient safety, DICOM workflow is an important update to current practice.

 
Keywords: 550 imaging/image analysis: clinical • 465 clinical (human) or epidemiologic studies: systems/equipment/techniques • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound)  
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